Advances in Machine Learning and Cybernetics pp 861-870
Context-Sensitive Kernel Functions: A Distance Function Viewpoint
- Cite this paper as:
- Vanschoenwinkel B., Liu F., Manderick B. (2006) Context-Sensitive Kernel Functions: A Distance Function Viewpoint. In: Yeung D.S., Liu ZQ., Wang XZ., Yan H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science, vol 3930. Springer, Berlin, Heidelberg
This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted) distance functions. It is shown how a weighted string distance can be decomposed and subsequently used in different kernel functions and how these kernel functions correspond to real kernels between the continuous, real representations of the symbolic, context-based representations of the vectors.
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